Relatively little research explores group testing of correlated samples.
[1]}, a large-scale observational study, observes that individuals in the same social groups are often pooled together in large-scale screening. It discovers that samples with low viral load, each of which would have likely been missed if it were the only positive sample in the pool, were detected when pooled with high-viral-load samples. This led to higher sensitivity than would have occurred with pools containing independently infected samples.
[2]} uses a simple example with pairwise correlation and perfect test accuracy to illustrate reduced test consumption in the Dorfman procedure. [3]} uses simulations to show that correlation improve the efficiency of hierarchical and matrix-based group testing.
[4]} uses simulation to show that pooling individuals with similar prevalence levels reduces costs.
Building on these observations, [5]} models sample collection at a testing site as a regenerative process and calculates the cost efficiency of different testing protocols, while assuming perfect test accuracy.
[6]} models a constant pairwise correlation in infections using a Beta-Binomial distribution for the number of positives in a pool, and demonstrates that this correlation improves efficiency.
The paper does not study the effect of correlation on sensitivity: it assumes
a fixed sensitivity for pooled tests and does not model the effect of pool size or correlation on sensitivity.
